Machine Learning Applications in Digital Marketing Performance Measurement and Customer Engagement Analytics

Abstract

Machine learning applications have become increasingly integral to digital marketing performance measurement and customer engagement analytics due to the volume, velocity, and behavioral richness of digital interaction data. This study quantitatively examined the relationships between machine learning–derived engagement indicators, marketing exposure variables, and digital marketing performance outcomes using an observational dataset of 1,250 user-level records. Descriptive analysis revealed substantial behavioral variability, with interaction frequency averaging 14.6 interactions per user (SD = 6.3), engagement recency averaging 4.1 days (SD = 2.7), and session depth averaging 6.2 actions per session (SD = 2.1). Reliability assessment confirmed strong internal consistency across all multi-item constructs, with Cronbach’s alpha values ranging from 0.82 for conversion outcomes to 0.93 for the customer value index. Multivariate regression results indicated that engagement intensity was the strongest predictor across all performance outcomes, with standardized coefficients of 0.38 for conversion, 0.42 for retention, and 0.41 for customer value, all statistically significant at p < .001. Engagement frequency also demonstrated positive and significant effects, with coefficients of 0.31 for conversion and 0.29 for customer value. Engagement recency showed a negative association across models, with coefficients ranging from −0.17 to −0.23, indicating declining performance as interaction gaps increased. Exposure frequency exhibited smaller yet significant effects, with coefficients between 0.18 and 0.24. The regression models demonstrated satisfactory explanatory power, reporting adjusted R² values of 0.39 for conversion, 0.44 for retention, and 0.47 for customer value. Hypothesis testing results showed that 11 of the 12 proposed hypotheses were supported. Overall, the findings demonstrated that machine learning–enabled engagement analytics substantially enhanced digital marketing performance measurement by capturing behavioral mechanisms underlying conversion, retention, and value creation more effectively than exposure-based metrics alone.